There's quite a bit of magic hiding under this. For e.g., try creating a NumPy array with different orderings, and the metadata looks the same:

import asdf

import numpy as np

x = np.array([[1, 2], [3, 4]], order="C")

y = np.array([[1, 2], [3, 4]], order="F")

tree = {"x": x, "y": y}

af = asdf.AsdfFile(tree)

af.write_to("example.asdf")

and you get in the metadata no distinction between the two arrays even though things like byteorder are included:

x: !core/ndarray-1.0.0

  source: 2

  datatype: int64

  byteorder: little

  shape: [2, 2]

y: !core/ndarray-1.0.0

  source: 0

  datatype: int64

  byteorder: little

  shape: [2, 2]

This makes me wonder what it's actually storing - is it actually doing something like pickling the NumPy array?